#A.R.

[fronzennews]

AI Simplify Science Communication Enhancing Understanding and Trust

  The concept of utilizing Artificial Intelligence (AI) to simplify science communication and restore public trust in science is an active area of research. The complexity of scientific language often poses significant barriers to public understanding, necessitating more accessible communication methods. Recent studies highlight AI’s innovative role in bridging the gap between scientific discourse and public comprehension.

Simplifying Scientific Content with AI

AI is proving to be a powerful tool for producing simplified scientific summaries that enhance public comprehension. Research led by David Markowitz, an associate professor of communication at Michigan State University, demonstrates the ability of AI to generate clear, concise summaries of scientific articles. The study utilizes the advanced language model GPT-4 by OpenAI to distill complex scientific content into understandable formats. The Role of AI in Generating Summaries In a striking demonstration of AI's capabilities, Markowitz's research reveals that AI-generated summaries significantly outperform traditional human-written content in terms of readability. These AI summaries use simpler language and more common terms, making them easier for the general public to grasp the core ideas of scientific studies.

Photo by Pavel Danilyuk Improving Public Comprehension Empirical evidence supports that participants exposed to AI-generated summaries were more likely to provide accurate and detailed recaps of the content compared to those who read human-authored summaries. This gain in understanding underlines AI's potential to foster better public engagement with science by demystifying complex topics.

Impact of Simplified Science Communication on Public Trust

The way scientific information is communicated can have profound implications for how the public perceives scientists and their work. Markowitz’s experiments indicate a direct correlation between simplified science communication through AI and the enhanced credibility of scientists in the eyes of the public. Credibility and Trustworthiness of Scientists Studies reveal that scientists whose research is described using simpler terminology are viewed as more credible than those whose work is communicated through complex jargon. As clarity increases, so does public trust, suggesting that effective communication strategies can promote a more favorable outlook towards scientific endeavors. Encouraging Engagement in Scientific Issues Improved science communication has the potential to drive greater public engagement with scientific issues. By making science more relatable and accessible, individuals may feel more empowered to discuss scientific topics, participate in dialogues, and support evidence-based policies.

Critical Engagement and Evaluation of AI in Science Communication

As AI's role in science communication expands, it is crucial to critically evaluate its capabilities and address potential biases in the information it disseminates. A comprehensive framework for assessing AI-generated content focuses on the reliability of data sources, recency, and adherence to scientific rigor. Framework for Evaluating AI Technologies Implementing a structured framework involves evaluating the underlying data from which AI derives its summaries. Considering the reliability of these data sources is integral to ensuring that the public receives accurate representations of scientific knowledge. The Role of Anthropomorphism in AI Interaction How users perceive AI can greatly influence their trust levels and the overall effectiveness of communication. Understanding the anthropomorphism of AI—how people interact with and attribute human-like qualities to AI technologies—is fundamental, especially for users with varying levels of digital literacy.

Addressing Misinformation and Ethical Considerations

While AI has the potential to simplify scientific communication, it also introduces challenges surrounding misinformation and oversimplification. Balancing accessible communication with the complexity inherent in scientific discourse is crucial to avoid misinterpretations. The Importance of Transparency in AI-Generated Content Transparency is essential in AI-generated communications. It is vital for AI systems to clarify the origins of their summaries to mitigate risks of bias and misunderstanding, ensuring that consumers of this information can trust its validity. Establishing Norms for AI in Academic Publishing The academic publishing industry is currently navigating the integration of AI into its processes. There is a pressing need to establish norms that scrutinize the use of AI in producing and presenting scientific content to safeguard the integrity of academic communication.

AI Applications in Public Health Communication

AI technology is also making strides in addressing public health messaging and risk perception. Research into how AI can 'Extract Insights from Digital Public Health Data' illustrates how AI can analyze massive datasets and provide meaningful insights that inform public health policies. Leveraging AI for Analyzing Public Health Data AI's ability to process and analyze large volumes of data holds considerable promise for enhancing the effectiveness of public health communications. Studies show that AI can identify trends, assess risks, and support the decision-making processes of public health officials. Strategies for Effective Health Communication Integrating AI into public health communication strategies can streamline the dissemination of information, ensuring transparency and combating misinformation. Adopting best practices in health communication can help in building public trust and improving health outcomes.

Photo by Merlin Lightpainting

Building Trust and Engagement in Science Communication

Various initiatives and workshops aimed at enhancing science communication through AI are emerging, focusing specifically on building trust between scientists and the public. Events emphasize the importance of dialogue, inclusivity, and accessibility in scientific discourse. Innovative Events and Symposiums Conferences like the "Civic Science & Ethics in the Age of AI: Building Trust" symposium at the University of Notre Dame aim to develop innovative strategies for effective science communication. These events foster productive exchanges between scientists and the public, highlighting the essential role of trust in science communication. Government and Organizational Efforts in AI Innovation Organizations such as the National Institute of Standards and Technology (NIST) are spearheading efforts to promote responsible AI development. The NIST AI Innovation Lab works on establishing standards and guidelines that contribute to building trust in the use of AI across various sectors of society.

The Future of AI in Science Communication

The integration of AI into science communication presents an exciting opportunity to simplify complex scientific information and enhance public understanding. However, ethical considerations, transparency, and the preservation of scientific nuance must remain paramount as the technology continues to evolve. As we look forward, the role of AI in science communication is expected to expand, ultimately contributing to a more informed public and deeper engagement with scientific issues. For more similar news, I invite you to visit my blog at FROZENLEAVES NEWS. ```   Read the full article

[curryvillain]

Jamaican Illustrator @Graytness Shares Augmented Reality "The Future Of Jamaica" Piece

To say Jamaica is a talented Country is an understatement. The Island boasts some of the most creative minds, with skillsets that have rivaled many the world over, and influenced generations. From Music, to Fashion, to Food, to Art, it’s never normal when it comes to Jamaicans. When it comes to Art, Jamaica has produced some very special individuals. From Cecil Baugh, to Edna Manley, to Albert…

[hvezdnastreka]

A date with Cardassia

[typewriter-worries]

It's world poetry day so here are some (more) of my favorite poems:

What You Missed That Day You Were Absent from Fourth Grade by Brad Aaron Modlin

All Trains Are Going Local by Timothy Liu

Rural Boys Watch the Apocalypse by Keaton St. James (@boykeats)

HOPE YOU’RE WELL. PLEASE DON’T READ THIS. by Lev St. Valentine (@dogrotpdf)

Time of Love by Claribel Alegría

Every Job Has a First Day by Rebecca Gayle Howell

ALL THAT WANTING, RIGHT? by Devin Kelly

Reading by A.R. Ammons

things i want to ask you by Helga Floros

Night Bird by Danusha Laméris

Prayer for Werewolves by Stephanie Burt

The Two Times I Loved You the Most In a Car by Dorothea Grossman

The Yearner by Rachel Long

If I Had Three Lives by Sarah Russell

I Dream on a Crowded Subway Train with My Eyes Open But My Body Swaying by Chen Chen

We Have Not Long to Love by Tennessee Williams

Jesus at the Gay Bar by Jay Hulme

Cracks by Dieu Dinh

and here's part one <3

[realcleverscience]

AI, Automation, Jobs, and the Every-Skill Worker

Been thinking a lot about AI in light of recent successes like stable diffusion and chatgpt, and how it seems we’re undoubtedly approaching the technological singularity, where science and technology is growing exponentially fast.

I am extremely concerned about the short-term implications and potential for mass layoffs. If we get AIs that can suddenly do, say, 85% of a job, it makes it easier for companies to lay off 85% of the people doing those jobs.

So what will people do?

I think one of the few things working in human’s favor right now is the difference between AI and Robotics. While both are improving at extraordinary rates (esp with AI helping robotics), AIs are a lot cheaper than robotics because it’s just code. You can duplicate code endlessly for almost zero cost. In contrast, robots are machines, which not only require code, but also lots of physical resources and face physical risks.

In short, even though we don’t yet have robots that are very good at picking up diverse objects, we’ll definitely get to that point soon. However, people are just as good as robots at many of these tasks. So considering the costs of robots, esp general purpose robots, not to mention lag in true AI to problem solve... I could see a scenario where humans and AI work together, instead of just robots and AI.

In particular, I could see AI and augmented reality creating a new class of “every skill worker”. For example, let’s talk about changing a tire. I’ve personally only done it once, years ago. I had no idea what I was doing. My dad helped guide me through the process, standing at the side, giving instructions. And voila, a car idiot like me was able to change a tire. Most of us could *physically* do LOTS of “skilled” jobs if we were just following step by step directions from a mentor right next to us. The problem is how to get those directions. These days, a lot of people go to places like youtube to watch lessons on how to do skilled jobs, like, say, fixing a broken washing machine or reprogramming a buggy piece of tech. The drawback, of course, is that it’s not catered to each person and their particular needs. It also doesn’t provide feedback like, “hey, make sure to screw that extra tight” or “I noticed you forgot to do xyz”. But imagine an AI that works with AR to be your guide. For instance, many people have been writing about how it feels like they’re working with a partner when they code with chatgpt. Imagine something like that but a program that can see and recognize what you’re doing. It can give step by step directions, see what step you’re on, see if you missed something, and provide visual guides for each step on the ar display.

This could mean that any half-intelligent person could be dropped into any number of complex skilled jobs and be able to just get started. Imagine a temp agency where one day you’re dropped into a washing machine factory or fixing a car, or installing advanced technology on day 1.

I know we haven’t heard much about AR in a while (this post feels like me getting excited about Google Glass 10 years too late... or too early?), but I think it could still prove a useful tool once its ready for prime time. Additionally, I want to note that robots will still be used in many and increasing roles, but I think there may be some where just hiring a person is just as economical, especially if AI and a depression cause some economic changes. E.g. There could be changes which lower wages without lowering quality of life, and which could make humans more economical. For instance, if we instituted a UBI, lower wages wouldn’t really matter. If we automated a lot of expensive services (esp education and medicine), this could lower cost of living and bring down wages. And of course, wages can be lower *with* a lower quality of life, such as mass layoffs and a depression due to AI.

Sooo... while I certainly have my preference for how I’d like to see things evolve, my main point here is that there *could* be a role for a humans, and that AI and VR could give everyone “super skills”. And hey, outside the dread of staying alive in an advanced-AI capitalist system - it’d be pretty cool to be able to do just about anything.

[mellxncollie]

PAINLAND WEEK - DAY TWO - MYTHS/LEGENDS → Sophus Helle, Gilgamesh — tablet XII, lines 87-95

[apocryphics]

a.r. ammons

[feedingicetothedog]

so like a really long time ago i saw this video of anne rice talking about vampire creativity and she basically said that vampires can't make anything artistically valid, that that type of creativity is reserved for human beings and this ep really made me think about that bc these vampires really want to make things but then they get frustrated when what they make isn't good and they can't find what they're looking for in their work but what's missing is the humanity. louis can see things that human beings can't bc of his vampiric abilities but that doesn't automatically give him a leg up irt making art, his work is more documentation than capturing singular moments. and he's frustrated that he can't connect w his subjects and it furthers his feelings of alienation from humanity bc why can't he just do it? why can't he convey the things he's trying to w his photos? and then he covets human work bc the spark required to make it is unavailable to him

[apoemaday]

Way to Go

by A.R. Ammons

West light flat on trees: bird flying deep out in blue glass: uncertain wind stirring the leaves: this is the world we have: take it

[fronzennews]

What is Generative AI and Its Key Applications Today

Generative AI, a rapidly evolving field of artificial intelligence, is gaining significant attention for its remarkable ability to create original content across various formats, including text, images, and videos. This innovative technology is driving advancements in numerous sectors, making it crucial to understand its definition, functionality, and applications in today's landscape.

Definition and Functionality of Generative AI

Generative AI refers to a subset of artificial intelligence that employs generative models to produce unique and original content in response to user prompts. This technology distinguishes itself from other AI forms by its focus on content generation, utilizing deep learning techniques, particularly neural networks, to analyze extensive datasets and replicate identified patterns to produce outputs resembling human creativity.

What is Generative AI?

Essentially, generative AI is a branch of artificial intelligence aimed at generating new data that mimics the characteristics of existing data. This technology enables the production of text, media, and various other content forms, effectively allowing machines to create materials that can be indistinguishable from those crafted by humans.

Computer AI Why I keep losing How Does Generative AI Work? The operation of generative AI involves several key steps, allowing it to generate content tailored to the needs of users: - Prompt Input: The process begins when users provide a prompt, which can be text, an image, a video, or other data types. This input serves as a guide for the AI to generate the desired output. - Neural Networks: Generative AI employs intricate neural network architectures, such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformers, to process the input data and produce new content based on the learned patterns. - Training: These models are typically trained on extensive, often unlabeled datasets. During this phase, the AI learns to identify patterns and structures within the data, enabling it to generate outputs that reflect human-like creativity and decision-making processes.

Key Technologies and Models Used in Generative AI

A variety of cutting-edge technologies and models underpin generative AI, enhancing its capability to create innovative content. Generative Adversarial Networks (GANs) GANs are a pioneering class of generative models comprised of two competing neural networks: a generator and a discriminator. The generator is responsible for creating content, while the discriminator evaluates the authenticity of this content. As these two networks engage in a continuous interplay, they both improve over time, resulting in increasingly sophisticated output from the generator. Transformers and Large Language Models (LLMs) Recent advancements in transformer architectures have significantly bolstered the evolvement of generative AI, especially in natural language processing. Large Language Models, such as OpenAI's GPT-4, exemplify this trend by enabling the AI to generate high-quality text and media based on user-defined natural language prompts.

Applications and Use Cases of Generative AI

Generative AI applications are diverse and transformative, permeating various industries and sectors. Creative Industries In the realm of creativity, generative AI is making waves in fields like writing, art, and fashion. Tools such as DALL-E and Midjourney are notable for their ability to create visual art from simple text descriptions, thereby revolutionizing the creative process and inspiring new artistic avenues. Healthcare Generative AI's role in healthcare extends to personalizing treatments and enhancing educational methodologies within medical education. By generating specific data patterns relevant to patients, AI can assist healthcare professionals in crafting tailored treatment plans. Software Development In software development, generative AI is proving invaluable by aiding programmers in code generation and automating certain tasks. Tools like ChatGPT and Copilot exemplify this application, enhancing productivity and allowing developers to focus on more complex problem-solving challenges. Customer Service and Marketing Generative AI has also found applications in customer service, particularly through chatbots that engage users and address inquiries efficiently. Additionally, it is used to create captivating marketing content, driving engagement and interest in various brands and products. Education Explorations into education have shown that generative AI can enhance teaching methods through adaptive learning techniques. AI-generated educational content can provide personalized material that caters to individual learning needs, thereby improving overall educational experiences..

The Benefits and Limitations of Generative AI

While generative AI holds substantial promise, it is essential to consider both its advantages and challenges. Benefits of Generative AI One of the most notable benefits of generative AI is its capability to foster creativity and innovation. The technology enables users to explore novel solutions and produces a wide array of outputs, enhancing sector-wide creativity. It also streamlines content creation processes by allowing users to articulate their requests in simple language and obtain tailored results effortlessly. Limitations and Ethical Concerns However, concerns surrounding the misuse of generative AI technologies are paramount. Issues such as the generation of deepfakes, misinformation, and potential cybercrime highlight the necessity for ethical governance and responsible deployment of these technologies. Furthermore, intellectual property rights and data bias in AI models have surfaced as critical issues that require attention and resolution.

Future Developments in Generative AI

The trajectory of generative AI is poised for significant expansion across multiple domains. Expanding into 3D Modeling and Product Design Generative AI is expected to greatly impact product design and 3D modeling, allowing for rapid creation of innovative designs and facilitating experimentation with new organizational models. Advancements in Supply Chains and Business Processes In the realm of business, generative AI can optimize processes and enhance supply chain management by supporting sophisticated risk assessments and opportunity analyses. Impacts on Drug Development and Digital Twins The potential for generative AI to contribute to drug discovery and the development of digital twins for accurate simulations opens up exciting avenues for research and development, with promising implications for future healthcare advancements.

Distinctions Between Generative AI and Other Types of AI

A thorough understanding of generative AI necessitates clarifying its distinctions from other artificial intelligence forms. Generative AI vs. General AI While generative AI focuses primarily on creating new content through machine learning techniques, General AI (Artificial General Intelligence) pertains to systems that possess human-like intelligence and autonomy—still largely a theoretical domain. Generative AI vs. Machine Learning Generative AI is specifically a subfield of AI that employs machine learning techniques to produce new data, distinguishing it from other machine learning applications that primarily emphasize decision-making, pattern recognition, and prediction. Generative AI vs. Regenerative AI Generative AI centers on creativity and originality, generating fresh content. In contrast, Regenerative AI focuses on building self-improving AI systems that are sustainable and durable without continual human intervention. In summary, generative AI offers remarkable opportunities for innovation and creativity while presenting various challenges related to ethical concerns and biases. Its applications across diverse industries indicate its potential to transform how we create and interact with content. The ongoing evolution of generative AI is paving the way for exciting developments, and as we advance, the importance of responsible usage cannot be understated. To discover more similar news, visit my blog at FROZENLEAVES NEWS. ``` Read the full article

[yourfavebooklrsfavebooklr]

8 amazing trans books for trans day of visibility!

[transbookoftheday]

Agender Books To Read For Agender Pride Day

Happy Agender Pride Day! Here are some books with agender characters you should read:

Book Titles:

The Heartbreak Bakery by A.R. Capetta

The Evolving Truth of Ever-Stronger Will by Maya MacGregor

In the Care of Magic by Robin Jo Margaret

Our Bloody Pearl by D.N. Bryn

When the Angels Left the Old Country by Sacha Lamb

Something Spectacular by Alexis Hall

The Thread That Binds by Cedar McCloud

Werecockroach by Polenth Blake

[thunderstruck9]

A.R. Penck (German, 1939-2017), Rock I, 1984. Dispersion on canvas, 160 x 130 cm.

[transparentdreamruins]

"There is my heart, and then there is you, and I'm not sure there is a difference."

~ A.R. Asher

[dame-de-pique]

A.R. Kingsford & Son - The Winding River, n.d.

[humanoidhistory]

Illustration by A.R. Lowe from Science Fiction Monthly, November 1975.